An image processing device includes: an image data acquisition unit for acquiring SPECT image data of a brain; a brain-region ROI definition unit for defining a brain-region ROI in the SPECT image; a striatum ROI definition unit for defining a striatum ROI in the SPECT image; and a threshold determination unit for, based on counts in the SPECT image's background which is the brain-region ROI except the striatum ROI, determining a threshold for distinguishing ventricles and sulci in the SPECT image; a region distinction unit for distinguishing between a region whose number of counts is smaller than or equal to the threshold and a region whose number of counts is larger than the threshold.

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

A computer-implemented method for determining a flow rate for a given vessel includes obtaining, via a processor, dynamic three-dimensional (3D) images of a subject utilizing nuclear medicine imaging. The method also includes obtaining, via the processor, injection parameters for a radiotracer bolus injected into the subject via an automated injector. The method further includes generating, via the processor, time activity curves (TACs) for the radiotracer bolus from the 3D images. The method even further includes estimating, via the processor, the flow rate for the given vessel based on a morphology of the one or more TACs and the injection parameters.

Provided is an image processing device including a matching unit that performs matching processing between a predetermined pattern on a surface of a 3D model of a biological tissue including an operating site generated on the basis of a preoperative diagnosis image and a predetermined pattern on a surface of the biological tissue included in a captured image during surgery, a shift amount estimation unit that estimates an amount of deformation from a preoperative state of the biological tissue on the basis of a result of the matching processing and information regarding a three-dimensional position of a photographing region which is a region photographed during surgery on the surface of the biological tissue, and a 3D model update unit that updates the 3D model generated before surgery on the basis of the estimated amount of deformation of the biological tissue.

A diagnosis support system includes a calculator configured to calculate position information indicating a positional relationship between a biological sensor and a predetermined region of a measurement target; and an extractor configured to extract, from biological information already diagnosed, biological information that is associated with position information, which is similar to the position information calculated by the calculator.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.

An inverse visualization of a time-resolved angiographic image data set of a vascular system of a patient that was recorded by a medical imager during the flow of a contrast medium through the vascular system is provided. The time-resolved angiographic image data set of the vascular system has a temporal sequence of frames of the vascular system corresponding to the contrast medium filling process. A data set from bolus arrival times for each pixel or voxel is determined. The bolus arrival time corresponds to the time in the temporal sequence at which a predetermined contrast enhancement due to the contrast medium filling first occurs. A data set of temporally inverted bolus arrival times with respect to the contrast medium filling is determined for each pixel or voxel, resulting in a temporally inverted sequence of frames with respect to the contrast medium filling. The time-resolved angiographic image data set in the temporally inverted sequence is visualized.

The present invention relates to an X-ray imaging system (10), comprising an X-ray image acquisition unit (20); and a processing unit (30). The X-ray image acquisition unit is configured to operate in at least one scout scan mode of operation. The X-ray image acquisition unit is configured to operate in a plurality of diagnostic image acquisition modes of operation. The X-ray image acquisition unit is configured to operate in a specific scout scan mode of operation of the at least one scout scan mode of operation to acquire a scanogram of a body part of a patient. The X-ray image acquisition unit is configured to provide the scanogram to the processing unit. The processing unit is configured to execute a trained machine learning algorithm to analyse the scanogram to select a specific diagnostic image acquisition mode of operation of the plurality of diagnostic image acquisition modes of operation, wherein the selection comprises a determination of one or more probabilities for one or more diseases or conditions associated with the body part f the patient. The X-ray image acquisition unit is configured to operate in the specific diagnostic image acquisition mode of operation o acquire diagnostic image data of the body part of the patient.

Systems and methods are disclosed for assessing cardiovascular disease and treatment effectiveness based on adipose tissue. One method includes identifying a vascular bed of interest in a patient's vasculature; receiving a medical image of the patient's identified vascular bed of interest; identifying adipose tissue in the received medical image; receiving a geometric vascular model comprising a representation of the patient's identified vascular bed of interest; and computing an inflammation index associated with the geometric vascular model, using the identified adipose tissue.